The present disclosure relates to a method for providing a signal quality degree associated with an analyte value measured in a continuous monitoring system as well as to related methods for determining an amount of insulin to be delivered and for calibrating the continuous monitoring system. The present disclosure further relates to a computer program product as well as to a sensor unit and to a continuous monitoring system which apply at least one of the mentioned methods.
The methods and devices according to the present disclosure may primarily be used for a continuous monitoring of the analyte glucose, wherein analyte values are measured by a biosensor in an interstitial fluid subcutaneously and/or in vivo, wherein the biosensor is implantable or partially implantable. The methods and devices according to the disclosure may be applied both in the field of home care as well as in the field of professional care, such as in hospitals. However, other applications are also feasible.
Monitoring certain body functions, more particularly monitoring one or more concentrations of certain analytes, plays an important role in the prevention and treatment of various diseases. Without restricting further possible applications, the methods and devices according to the disclosure are described in the following with reference to a continuous monitoring of the analyte glucose in an interstitial fluid by using a biosensor.
The glucose monitoring may be performed by using electrochemical sensors as well as optical measurements. Examples of electrochemical biosensors for measuring glucose, specifically in blood or other body fluids, are known from U.S. Pat. Nos. 5,413,690 A, 5,762,770 A, 5,798,031 A, 6,129,823 A and U.S. 2005/0013731 A1. For example, an active sensor region is directly applied to a measurement site which is, generally, arranged in an interstitial tissue, and may convert glucose into an electrically charged entity by using an enzyme, in particular into glucose oxidase, generally abbreviated to “GOD.” As a result, the detectable charge in the electrochemical biosensor may be related to the glucose concentration and can, thus, be used as a measurement variable. Examples of such kinds of transcutaneous measurement systems are described in U.S. Pat. No. 6,360,888 B1 or U.S. 2008/0242962 A1.
As generally known, glucose measurements may be performed as “spot measurements.” For this purpose, a sample of a body fluid is taken from a user, i.e., a human or an animal, in a targeted fashion and examined with respect to the analyte concentration in vitro and/or in a transdermal fashion. In contrast, the continuous measuring of the analyte glucose in the interstitial fluid, also referred to as “continuous glucose monitoring” or abbreviated to “CGM,” has been established as a method for managing, monitoring, and controlling a diabetes state. For this purpose, the continuous measuring of the analyte value in the interstitial fluid is performed via a transcutaneous or a subcutaneous system in a subcutaneous fashion and/or in vivo. Accordingly, the biosensor or at least a measuring portion thereof may be arranged under the skin of the user. Generally, an evaluation and control part of the system, also referred to as a “patch,” may be located outside the body of a user. The biosensor is generally applied by using an insertion instrument, which is, in an exemplary fashion, described in U.S. Pat. No. 6,360,888 B1. However, other types of insertion instruments are also known. Further, a control part may be required. Such a control part may be located outside the body and have to be in communication with the biosensor. Generally, communication is established by providing at least one electrical contact between the biosensor and the control part, wherein the contact may be a permanent electrical contact or a releasable electrical contact. Other techniques for providing electrical contacts, such as by using appropriate spring contacts, are known and may also be applied.
In continuous glucose measuring systems, the concentration of the analyte glucose may be determined by employing an electrochemical sensor comprising an electrochemical cell having a working electrode and a counter electrode. Herein, the working electrode may have a reagent layer comprising an enzyme with a redox active enzyme co-factor adapted to support an oxidation of the analyte in the body fluid. The reagent layer may, further, comprise or redox mediator which, typically, may act as an electron acceptor. The redox mediator can react with the enzyme co-factor and may, thus, transport electrons received from the enzyme co-factor to a counter electrode surface, such as by diffusion. At the counter electrode surface, the redox mediator may be oxidized and the transferred electrons can, consequently, be detected as a current. The current may, preferably, be related to a concentration of the analyte in the body fluid, e.g., such as being proportional thereto. U.S. 2003/0146113 A1 and U.S. 2005/0123441 A1 disclose examples for this process.
According to S. Shanthi and D. Kumar, Neural Network Based Filter for Continuous Glucose Monitoring: Online Tuning with Extended Kalman Filter Algorithm, WSEAS Transactions on Information Science and Applications Vol. 9, 2012, p. 199-209, an evaluation of the accuracy of continuous glucose monitoring (CGM) systems is complex for two primary reasons. First, the CGM systems assess fluctuations of the blood glucose level indirectly by measuring the concentration of interstitial glucose but are calibrated via self-monitoring in order to approximate the blood glucose level. Second, CGM data reflect an underlying process in time and usually consist of ordered-in-time highly interdependent data points. Apart from a physiological time lag and an improper calibration, random noise and errors, in particular due to sensor physics and sensor chemistry, might affect the accuracy of the CGM data. As a result, the performance of CGM signals, in particular with respect to a hypoglycemic alert generation and to a control input into an artificial pancreas, may be deteriorated. Related studies have shown that the percentage of false alarms and missing alarms is about 50 percent, which the authors primarily assign to insufficient filtering.
U.S. 2008/249384 A1 discloses glucose monitoring systems for continuously measuring the glucose concentration in a patient's blood. The system is adapted to communicate with one or more sensors for transcutaneous insertion into a patient and for producing sensor signals related to the glucose concentration. The system comprises an electronic calculator unit and a display for displaying the measured glucose concentration. The electronic calculator unit further comprises means for calculating an estimate of the uncertainty, i.e., the degree of accuracy of the glucose measurement, and the display is configured for displaying an interval representing the uncertainty.
U.S. 2005/004439 A1 discloses a method of calibrating glucose monitor data including collecting the glucose monitor data over a period of time at predetermined intervals. It also includes obtaining at least two reference glucose values from a reference source that temporally correspond with the glucose monitor data obtained at the predetermined intervals. Also included is calculating the calibration characteristics using the reference glucose values and corresponding glucose monitor data to regress the obtained glucose monitor data. And, calibrating the obtained glucose monitor data using the calibration characteristics. In preferred embodiments, the reference source is a blood glucose meter, and the at least two reference glucose values are obtained from blood tests. In additional embodiments, calculation of the calibration characteristics includes linear regression and, in particular embodiments, least squares linear regression. Alternatively, calculation of the calibration characteristics includes non-linear regression. Data integrity may be verified and the data may be filtered.
U.S. 2014/121989 A1 discloses systems and methods for measuring an analyte in a host. More particularly, the disclosure relates to systems and methods for processing sensor data, including calculating a rate of change of sensor data and/or determining an acceptability of sensor or reference data.
U.S. 2012/215462 A1 discloses systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.
S. Shanthi et al., s. o., deal with a removal of errors due to various noise distributions in CGM sensor data. A feed forward neural network is trained with an Extended Kalman Filter algorithm to nullify the effects of white Gaussian, exponential and Laplace noise distributions in CGM time series. The process and measurement noise covariance values incoming signal. This approach answers for an inter-person and intra-person variability of blood glucose profiles. The neural network updates its parameters in accordance with a signal-to-noise-ratio of the incoming signal. The performance of the proposed system is analyzed with root mean square as a metric and has been compared with previous approaches in terms of time lag and smoothness relative gain. The new mechanism enables the application of CGM signals to hypoglycemic alert generation and input to an artificial pancreas.
U.S. 2014/182350 A1 discloses a method for determining an end of life of a CGM sensor which includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. Thus, this method is directed to solving the problem of determining the status or time for which the end of life of a sensor is near, so that a user may be informed that the sensor should be changed The plurality of risk factors are selected from a list including a number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and an error between reference BG values and EGV sensor values. For this purpose, quality metrics are scaled according to pre-determined weights and combined to produce an indicator of the overall quality of the computed glucose value, wherein the weights may be applicable to every metric and may show how indicative a metric is of end of life.
The present disclosure provides a method for providing a signal quality degree associated with an analyte value measured in a continuous monitoring system, a method for determining an amount of insulin to be delivered, a method for calibrating the continuous monitoring system, a computer program product, a sensor unit, and a continuous monitoring system which at least partially avoid the shortcomings of known methods and devices of this kind and which at least partially address the above-mentioned challenges.
In particular, it is desired that the methods and devices according to the present disclosure may be capable of providing a signal quality degree associated with a measured glucose which can be used in a decision whether an actually measured glucose value to which the signal quality degree is associated with may be considered in providing a specific signal by the continuous monitory system or not. For this purpose, it is, particularly, desired to implement a process which may be adapted of consecutively acquiring measured data and providing associated signal quality information, preferably in a nearly real, real-time or quasi-continuous approach, especially without user interaction, during the lifetime of the biosensor. The signal quality degree may also be able to assume a value between 0 and 1, wherein the value of 0 describes an insufficient quality while the value of 1 refers to a sufficient quality. In particular, it is desired that the signal quality degree may allow providing an improved accuracy value for use as a control input into an artificial pancreas and/or for a hypoglycemic alert generation throughout the lifetime of the biosensor.